TransformationModelLinear::TransformationModelLinear(
const TransformationModel::DataPoints & data, const Param & params)
{
params_ = params;
data_given_ = !data.empty();
if (!data_given_ && params.exists("slope") && (params.exists("intercept")))
{
// don't estimate parameters, use given values
slope_ = params.getValue("slope");
intercept_ = params.getValue("intercept");
}
else // estimate parameters from data
{
Param defaults;
getDefaultParameters(defaults);
params_.setDefaults(defaults);
symmetric_ = params_.getValue("symmetric_regression") == "true";
size_t size = data.size();
if (size == 0) // no data
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__,
"no data points for 'linear' model");
}
else if (size == 1) // degenerate case, but we can still do something
{
slope_ = 1.0;
intercept_ = data[0].second - data[0].first;
}
else // compute least-squares fit
{
vector<double> x(size), y(size);
for (size_t i = 0; i < size; ++i)
{
if (symmetric_)
{
x[i] = data[i].second + data[i].first;
y[i] = data[i].second - data[i].first;
}
else
{
x[i] = data[i].first;
y[i] = data[i].second;
}
}
double cov00, cov01, cov11, sumsq; // covariance values, sum of squares
double * x_start = &(x[0]), * y_start = &(y[0]);
gsl_fit_linear(x_start, 1, y_start, 1, size, &intercept_, &slope_,
&cov00, &cov01, &cov11, &sumsq);
if (symmetric_) // undo coordinate transformation:
{
slope_ = (1.0 + slope_) / (1.0 - slope_);
intercept_ = intercept_ * 1.41421356237309504880; // 1.41... = sqrt(2)
}
}
}
}
TransformationModelLinear::TransformationModelLinear(const TransformationModel::DataPoints& data, const Param& params) :
TransformationModel(data, params) // initializes model
{
data_given_ = !data.empty();
if (!data_given_ && params.exists("slope") && params.exists("intercept"))
{
// don't estimate parameters, use given values
slope_ = params.getValue("slope");
intercept_ = params.getValue("intercept");
}
else // estimate parameters from data
{
Param defaults;
getDefaultParameters(defaults);
params_.setDefaults(defaults);
symmetric_ = params_.getValue("symmetric_regression") == "true";
// weight the data (if weighting is specified)
TransformationModel::DataPoints data_weighted = data;
if ((params.exists("x_weight") && params.getValue("x_weight") != "") || (params.exists("y_weight") && params.getValue("y_weight") != ""))
{
weightData(data_weighted);
}
size_t size = data_weighted.size();
std::vector<Wm5::Vector2d> points;
if (size == 0) // no data
{
throw Exception::IllegalArgument(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION,
"no data points for 'linear' model");
}
else if (size == 1) // degenerate case, but we can still do something
{
slope_ = 1.0;
intercept_ = data_weighted[0].second - data_weighted[0].first;
}
else // compute least-squares fit
{
for (size_t i = 0; i < size; ++i)
{
points.push_back(Wm5::Vector2d(data_weighted[i].first, data_weighted[i].second));
}
if (!Wm5::HeightLineFit2<double>(static_cast<int>(size), &points.front(), slope_, intercept_))
{
throw Exception::UnableToFit(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, "TransformationModelLinear", "Unable to fit linear transformation to data points.");
}
}
// update params
params_.setValue("slope", slope_);
params_.setValue("intercept", intercept_);
}
}
